王 超
E-mail：wangchaoai@ustc.edu.cn
所属单位：人工智能与数据科学学院
个人主页：https://chaowang-ustc.github.io/
主要研究方向：大语言模型、数据挖掘、推荐系统
王超，现为中国科学技术大学人工智能与数据科学学院预聘副教授，博导。2016年于中科大少年班学院获学士学位，2022年于中科大计算机学院获博士学位（博士导师熊辉、陈恩红教授），2024年从广州市香港科大霍英东研究院、香港科技大学（广州）博士后出站（合作导师王炜教授）。
主要研究方向为大语言模型、数据挖掘、推荐系统等领域。近年来承担多项科研项目，包括国家自然科学基金青年基金C类、安徽省自然科学基金青年基金C类、认知智能全国重点实验室开放课题、CCF-阿里1688源宝合作基金、国家博士后面上基金等，作为子课题负责人参与国家科技重大专项1项。在相关领域国际重要期刊及会议发表论文40余篇，其中以第一作者及通讯作者身份发表CCF推荐的A类期刊和会议论文10余篇，已公开专利7项。曾获得中国计算机学会CCF优秀博士学位论文激励计划（CCF优博，每年仅10人）、《中国科学：信息科学》2022年度热点论文（每年4篇）、翟光龙学者基金、ICML 2025 Workshop best paper等荣誉。
近三年代表论著：
[1] Peng Du, YongWen Ren, Hui Liao,Hao Li, Hui Xiong, Chao Wang*. FLAME: Improving Legal Case Retrieval throughFactor-aware Graph Modeling and Mixture-of-Experts. Frontiers of ComputerScience (FCS), 2026, accepted. (CCF B)
[2] Yunchu Bai, Chao Wang*, YingSun, Chuan Qin, Wei Wu, Hui Xiong*. Graph-based Prompt Learning with Mixture ofExperts for Multi-task Corporate Profiling. ACM Transactions on KnowledgeDiscovery from Data, (ACM TKDD), 2026, accepted. (CCF B)
[3] Xi Chen, Chuan Qin, Ziqi Wang,Shasha Hu, Chao Wang, Hengshu Zhu, Hui Xiong. Beyond the Known: AnUnknown-Aware Large Language Model for Open-Set Text Classification. TheFourteenth International Conference on Learning Representations (ICLR-2026),2026, accepted. (CCF A)
[4] Ranxu Zhang, Junjie Meng, YingSun, Ziqi Xu, Bing Yin, Hao Li, Yanyong Zhang and Chao Wang*. MCLMR: AModel-Agnostic Causal Learning Framework for Multi-Behavior Recommendation.Proceedings of the 33rd World Wide Web Conference (WWW-2026), 2026, accepted.(CCF A)
[5] Jiaming Leng, Chao Wang*,Qi Zhang, Jianyao Hu, Leilei Ding, Bing Yin, Yanyong Zhang*. DecodingCitywide Electric Vehicle Charging Dynamics with Multi-View HeterogeneousSpatio-temporal Graph Networks. Frontiers of Computer Science (FCS), 2026,accepted. (CCF B)
[6] Lingfeng Liu, Yixin Song,Dazhong Shen, Bing Yin, Hao Li, Yanyong Zhang, Chao Wang*. RethinkingPopularity Bias in Collaborative Filtering via Analytical Vector Decomposition.ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2026), 2026,accepted. (CCF A)
[7] Yongwen Ren, Chao Wang*,Peng Du, Chuan Qin, Dazhong Shen, Hui Xiong*. Enhancing ConversationalRecommender Systems with Tree-Structured Knowledge and Pretrained LanguageModels. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2026),2026, accepted. (CCF A)
[8] Chao Wang*, Yixin Song, Jinhui Ye, Chuan Qin, Dazhong Shen, LingfengLiu, Xiang Wang, Yanyong Zhang. FACE: A general Framework for MappingCollaborative Filtering Embeddings into LLM Tokens. The Thirty-ninth AnnualConference on Neural Information Processing Systems (NeurIPS-2025), 2025,accepted. (CCF A)
[9] Wei Wu, ZhuoshiPan, Kun Fu, Chao Wang, Liyi Chen, Yunchu Bai, TianfuWang, Zheng Wang, Hui Xiong. TokenSelect: Efficient Long-ContextInference and Length Extrapolation for LLMs via Dynamic Token-Level KV CacheSelection. The 2025 Conference on Empirical Methods in Natural LanguageProcessing (EMNLP -2025), 2025, accepted. (CCF B)
[10] Wei Wu, Chao Wang*, Liyi Chen,Mingze Yin, Yiheng Zhu, Kun Fu, Jieping Ye, Hui Xiong, Zheng Wang.Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose ProteinUnderstanding with LLMs. ACM SIGKDD Conference on Knowledge Discovery and DataMining (KDD-2025), 2025, accepted. (CCF A)
[11] Hanzhe Li, Dazhong Shen, ChaoWang, Yuting Liu and Jingjing Gu. Can LLMs Enhance Fairness in RecommendationSystems? A Data Augmentation Approach. The 48th International ACM SIGIRConference on Research and Development in Information Retrieval (SIGIR-2025),2025, accepted. (CCF A)
[12] Leilei Ding, Zhipeng Tang, LeZhang, Dazhong Shen, Chao Wang, Ziyang Tao, Jingbo Zhou, Yanyong Zhang,Hui Xiong. Killing two birds with one stone: A Spatio-Temporal Prompt for theInductive Spatio-Temporal Extrapolation. The 30th International Conference onDatabase Systems for Advanced Applications (DASFAA-2025), 2025, accepted. (CCFB)
[13] Shengzhe Zhang, Liyi Chen,Dazhong Shen, Chao Wang*, Hui Xiong*. Hierarchical Time-Aware Mixture ofExperts for Multi-Modal Sequential Recommendation. Proceedings of the 32ndWorld Wide Web Conference (WWW-2025), 2025. (CCF A)
[14] Haoran Xin, Ying Sun, ChaoWang, Hui Xiong. LLMCDSR: Enhancing Cross-Domain Sequential Recommendation withLarge Language Models. ACM Transactions on Information Systems (ACM TOIS), 2025,accepted. (CCF A)
[15] Xi Chen, Chuan Qin, Chuyu Fang,Chao Wang, Chen Zhu, Fuzhen Zhuang, Hengshu Zhu, Hui Xiong. Job-SDF: AMulti-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking.The Thirty-eight Conference on Neural Information Processing Systems, (NeurIPS-2024),2024, accepted. (CCF A)
[16] Tianfu Wang, Liwei Deng, ChaoWang, Jianxun Lian, Yue Yan, Nicholas Jing Yuan, Qi Zhang, Hui Xiong. COMET:NFT Price Prediction with Wallet Profiling. ACM SIGKDD Conference on KnowledgeDiscovery and Data Mining, (KDD-2024), 2024, accepted. (CCF A)
[17] Leilei Ding, Dazhong Shen, ChaoWang*, Tianfu Wang, Le Zhang, Yanyong Zhang*. DGR: A General Graph DesmoothingFramework for Recommendation via Global and Local Perspectives. The 33rdInternational Joint Conference on Artificial Intelligence, (IJCAI-2024), 2024,accepted. (CCF A)
[18] Tianfu Wang, Qilin Fan, ChaoWang*, Long Yang, Leilei Ding, Nicholas Jing Yuan, Hui Xiong*. FlagVNE: AFlexible and Generalizable Reinforcement Learning Framework for NetworkResource Allocation. The 33rd International Joint Conference on ArtificialIntelligence, (IJCAI-2024), 2024, accepted. (CCF A)
[19] Xi Chen, Chuan Qin, ZhigaoyuanWang, Yihang Cheng, Chao Wang, Hengshu Zhu, Hui Xiong. Pre-DyGAE: Pre-trainingEnhanced Dynamic Graph Autoencoder for Occupational Skill Demand Forecasting.The 33rd International Joint Conference on Artificial Intelligence,(IJCAI-2024), 2024, accepted. (CCF A)
[20] Wei Wu, Chao Wang*, DazhongShen, Chuan Qin, Liyi Chen, Hui Xiong*. AFDGCF: Adaptive Feature De-correlationGraph Collaborative Filtering for Recommendations. The 47th International ACMSIGIR Conference on Research and Development in Information Retrieval,(SIGIR-2024), 2024, accepted. (CCF A)
[21] Yunqin Zhu, Chao Wang*, QiZhang, Hui Xiong*. Graph Signal Diffusion Model for Collaborative Filtering.The 47th International ACM SIGIR Conference on Research and Development inInformation Retrieval, (SIGIR-2024), 2024, accepted. (CCF A)
[22] Shuyao Wang, Yongduo Sui, ChaoWang, Hui Xiong. Unleashing the Power of Knowledge Graph for Recommendation viaInvariant Learning. Proceedings of the 31st World Wide Web Conference(WWW-2024), 2024. (CCF A)
[23] Shengzhe Zhang, Liyi Chen, ChaoWang, Shuangli Li, Hui Xiong. Temporal Graph Contrastive Learning forSequential Recommendation. Proceedings of the AAAI Conference on ArtificialIntelligence (AAAI-2024), 2024, accepted. (CCF A)
[24] Shasha Hu, Chao Wang*, ChuanQin, Hengshu Zhu, and Hui Xiong*. Super-node Generation for GNN-basedRecommender Systems: Enhancing Distant Node Integration via Graph Coarsening.The 29th International Conference on Database Systems for Advanced Applications(DASFAA-2024), Gifu, Japan, accepted, 2024. (CCF B)
[25] Chao Wang, Hengshu Zhu, ChenZhu, Chuan Qin, Hui Xiong. SetRank: A Setwise Bayesian Approach forCollaborative Ranking in Recommender System. ACM Transactions on InformationSystems (ACM TOIS), 42(2): 1-32 (2024). (CCF A)